The Perceived Impact of Environment on Health in Italy: a Penalized Ordinal Regression Approach

Abstract

Understanding how individuals perceive their living environment is a complex task, as it reflects both personal and contextual determinants. In this paper, we address this task by analyzing the environmental module of the Italian nationwide health surveillance system PASSI (Progressi delle Aziende Sanitarie per la Salute in Italia), integrating it with contextual information at the municipal level, including socio-economic indicators, pollution exposure, and other geographical characteristics. Methodologically, we adopt a penalized semi-parallel cumulative ordinal regression model to analyze how subjective perceptions are shaped by both personal and territorial determinants. The approach balances flexibility and interpretability by allowing both parallel and non-parallel effects while regularizing estimates to address multicollinearity and separation issues. We use the model as an analytical tool to uncover the determinants of positivity and neutrality in environmental perceptions, defined as factors that contribute the most to improving perception or increasing the sense of neutrality. The results are diverse. First, results reveal significant heterogeneity across Italian territories, indicating that local characteristics strongly shape environmental perception. Second, various individual factors interact with contextual influences to shape perceptions. Third, hazardous environmental factors, such as higher PM2.5 levels, appear to be associated with poorer environmental perception, suggesting a tendency among respondents to recognize specific environmental issues. Overall, the approach demonstrates strong potential for application and provides useful insights for environmental policy planning.

Figures (6)

• Figure 1: Perceived environmental health influence proportion by Region, ordered by the proportion reporting 'No' (neutral) influence.
• Figure 2: The top panel illustrates the variation in responses across quartile classes of contextual variables measured at the municipal level: average pollution, the FMI, and log-transformed population density. The bottom panel displays a scatter plot of log-population density against the average PM2.5 level for each municipality, stratified by quartile classes of the MFI. Points are colored according to the municipality's macro-area. For average pollution and MFI, higher quantiles indicate worse conditions.
• Figure 3: Proportion of respondents by LHU reporting a positive environmental impact on health and no difficulty making ends meet. Colour gradient encodes the mean LHU pollution level.
• Figure 4: On the left, point estimates of coefficients $\hat{\boldsymbol{B}}_\text{L} = \hat{\boldsymbol{\beta}}_{\text{L},-1}\hat{\boldsymbol{\beta}}_{\text{L},0}$ regarding different LHU fixed effects. On the right, the coefficients are rotated by $135^\circ$ and flipped in their second dimension. Colors identify different macro-regions in Italy. Text highlights selected LHUs.
• Figure 5: Ranking of LHUs based on the positivity and neutrality coordinates, i.e., after rotating by $135^\circ$ and flipping them in their second dimension. Colors identify different macro-regions in Italy. Bands report $95\%$ marginal confidence intervals.